ArticleScientific reports2016
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Machine learning reveals temperature as a key predictor of dengue risk across Thailand's provinces: A 20-year analysis.PLoS neglected tropical diseases · 2026Article
- Lagged and non-linear effects of Aedes surveillance indices on dengue incidence in Malaysia: evidence from sentinel-site data.Tropical medicine and health · 2026Article
- Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.Infectious Disease Modelling · 2026Article
- Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis.Frontiers in public health · 2026Review
- Article
- Ensemble approaches for short-term dengue fever forecasts: A global evaluation study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.BMC infectious diseases · 2025Article
- Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil.Tropical medicine and health · 2025Article
- Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.BMC public health · 2025Article
- Estimating Dengue Outbreak Thresholds in West Africa: A Comprehensive Analysis of Climatic Influences in Burkina Faso, 2018-2024.Tropical medicine and infectious disease · 2025Article
- Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach.Scientific reports · 2024Article
- Evaluating infectious disease forecasts with allocation scoring rules.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Dengue in Tomorrow: Predictive Insights From ARIMA and SARIMA Models in Bangladesh: A Time Series Analysis.Health science reports · 2024Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- Forecasting the incidence of dengue in Bangladesh-Application of time series model.Health science reports · 2022Article
- Modeling COVID-19 incidence with Google Trends.Frontiers in research metrics and analytics · 2022Article
- Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil.PLoS neglected tropical diseases · 2022Article
- Impact of environmental factors on the spread of dengue fever in Sri Lanka.International journal of environmental science and technology : IJEST · 2022Article
- A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles.Journal of the Royal Society, Interface · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.